Book Image

Mastering pandas - Second Edition

By : Ashish Kumar
Book Image

Mastering pandas - Second Edition

By: Ashish Kumar

Overview of this book

pandas is a popular Python library used by data scientists and analysts worldwide to manipulate and analyze their data. This book presents useful data manipulation techniques in pandas to perform complex data analysis in various domains. An update to our highly successful previous edition with new features, examples, updated code, and more, this book is an in-depth guide to get the most out of pandas for data analysis. Designed for both intermediate users as well as seasoned practitioners, you will learn advanced data manipulation techniques, such as multi-indexing, modifying data structures, and sampling your data, which allow for powerful analysis and help you gain accurate insights from it. With the help of this book, you will apply pandas to different domains, such as Bayesian statistics, predictive analytics, and time series analysis using an example-based approach. And not just that; you will also learn how to prepare powerful, interactive business reports in pandas using the Jupyter notebook. By the end of this book, you will learn how to perform efficient data analysis using pandas on complex data, and become an expert data analyst or data scientist in the process.
Table of Contents (21 chapters)
Free Chapter
1
Section 1: Overview of Data Analysis and pandas
4
Section 2: Data Structures and I/O in pandas
7
Section 3: Mastering Different Data Operations in pandas
12
Section 4: Going a Step Beyond with pandas

pandas styling

pandas allow for a wide variety of operations to be performed on DataFrames, making it easier to handle structured data. Another intriguing property of DataFrames is that they allow us to format and style regular rows and columns in tabular data. These styling properties help enhance the readability of tabular data. The Dataframe.style method returns a Styler object. Any formatting to be applied before displaying a DataFrame can be applied over this Styler object. Styling can be done either with in-built functions that have predefined rules for formatting or with user-defined rules.

Let's consider the following DataFrames so that we can take a look at pandas' styling properties:

  df = pd.read_csv("titanic.csv")
  df

The following screenshot shows the preceding DataFrame loaded into Jupyter Notebook:

DataFrame loaded into Jupyter Notebook...